UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)

Takuma Yoneda, Jeff Mitchell, Johannes Welbl, Pontus Stenetorp, Sebastian Riedel


Abstract
In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62.52% on the provisional test set (without additional human evaluation), and 65.41% on the development set. Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation. Retrieval is performed leveraging task-specific features, and then a natural language inference model takes each of the retrieved sentences paired with the claimed fact. The resulting predictions are aggregated across retrieved sentences with a Multi-Layer Perceptron, and re-ranked corresponding to the final prediction.
Anthology ID:
W18-5515
Volume:
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2018
Address:
Brussels, Belgium
Editors:
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
97–102
Language:
URL:
https://aclanthology.org/W18-5515
DOI:
10.18653/v1/W18-5515
Bibkey:
Cite (ACL):
Takuma Yoneda, Jeff Mitchell, Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018. UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF). In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 97–102, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF) (Yoneda et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5515.pdf